Researchers have developed MTRouter, a novel system designed to optimize the cost of multi-turn interactions with large language models. By jointly embedding interaction history and candidate models, MTRouter learns to predict model utility and select the most cost-effective model at each turn within a budget. Experiments demonstrated significant cost reductions, achieving 58.7% savings on ScienceWorld and 43.4% on Humanity's Last Exam compared to GPT-5, while maintaining competitive performance. AI
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IMPACT Optimizes LLM inference costs for multi-turn tasks, potentially enabling more complex applications within budget constraints.
RANK_REASON This is a research paper detailing a new method for optimizing LLM inference costs.